Investigating Willingness to Invest in Renewable Energy to Achieve Energy Targets and Lower Carbon Emissions

Abstract

There is a keen interest in renewable energy sources (RES) as a key aspect of reducing the emissions of greenhouse gases (GHG). Supporting policies have facilitated citizen investments in renewable energy, as such investments can make a substantial contribution to emissions reduction. The problem, however, is that the factors affecting citizen willingness- to invest in renewable energy are still uncertain and tend to constantly change, highlighting the need to perform studies on the subject more frequently. As citizen investments in RES can contribute to emissions reduction, the aim of this study is to understand the factors that affect the willingness of citizens to invest in renewable energy. Using simple random sampling, a representative sample of 1536 citizens in an EU country was administered structured questionnaires, and the results were analyzed using logistic regression. It was shown that willingness to invest is affected by both financial and non-financial factors, such as citizens’ agreement with the construction of renewable facilities near their residence, information sources for obtaining information about environmental and energy topics, satisfaction with the media’s coverage of renewable investments, and their occupation. Results from this study raise substantial policy implications and may be used to improve the design of strategies for attracting citizen investments.

1. Introduction

Gaseous compounds in the Earth’s atmosphere are able to absorb and emit infrared radiation, and, at the same time, high GHG concentrations of emissions are inextricably linked to climate change, the greatest challenge of our time. In the atmosphere of our planet, there are various anthropogenic greenhouse gases, of which the most important are carbon dioxide (CO₂), methane (CH₄), ozone (O₃), water vapor, nitrous oxide (N₂O), and fluorinated gases [1]. Carbon dioxide emissions mostly result from the combustion of fossil fuels and biomass [2]. Methane emissions, however, are associated with a much more complicated mix of anthropogenic and natural sources such as livestock, wetlands, and the combustion of fossil fuels and agricultural waste [3,4].

The policy response to the temperature increase has been rigorous over the last few years, and most nations have been implementing environmental policies and strict regulations to cut back on the emission of climate-harming gases [1]. Efforts to decarbonize economy sectors have given prominence to renewable energy sources (RES), which have lower emissions compared to those of fossil fuels. The European Union (EU) has frequently been characterized as a leader in renewable energy, and, as early as in 2009, it introduced the 2020 package, which set lofty objectives for year 2020. After these targets were met, however, the European Union set even more ambitious targets. An example would be the EU Climate Law of 24 June 2021, which established a binding target of 55% emissions reduction by 2030, as well as climate neutrality by 2050. Before setting these ambitious objectives, the EU established the Green Deal, which is its fundamental strategy to address the climate crisis and to become climate neutral by the year 2050. The Green Deal moves the EU closer to its 2050 objective of negative emissions and confirms its leadership in the global fight against climate change [5].

The ratification of international protocols, conventions, and agreements has resulted in systematic efforts that monitor the progress towards the reduction in climate-harming gases. In line with Article 5, paragraph 1 of the Kyoto protocol, every Annex I country is bound to develop a system to calculate anthropogenic emissions by sources, as well as removals by sinks of all greenhouse gases. Based on this kind of system, each country must prepare an annual inventory that reports emissions trends in relation to a pre-selected base year, as well as monitor the progress of current abatement measures in place for GHG reduction. The inventory covers the emissions of direct greenhouse gases (CO₂, CH₄, N₂O, PFCs, HFCs, SF₆, and NF₃) that are produced from the sectors of energy production, industry, product use, agriculture, land use, land-use change, and forestry. The inventory covers all years from the reference year to two years before the inventory is due [6].

In compliance with regulations and provisions in force, Greece, as an Annex I party, prepares inventory reports. The responsible body for the report is the Ministry of the Environment and Energy, and the preparation of reports is assigned to the National Technical University of Athens—specifically, the School of Chemical Engineering. The inventory measures emissions and compares them to the reference year of 1990, when GHG emissions amounted to 101.74 Mt CO₂ eq (including gases from land use, land-use change, and forestry, i.e., LULUCF). According to the latest inventory report for 2021, greenhouse gas emissions (without LULUCF) were estimated to be 77.50 Mt CO₂ eq, which represents a significant reduction of 25.48% in comparison to the levels recorded in 1990. With the inclusion of LULUCF, the decrease reaches 29.22%. More analytically, in 2021, emissions of carbon dioxide were 74.28% of the overall GHG emissions, showing a 25.48% decrease compared to 1990, whereas methane emissions comprised 14.61% of the overall GHG emissions, representing a 9.61% decrease compared to 1990. Moreover, nitrous oxide emissions were 4.93% of the overall GHG emissions, showing a 44.32% decrease compared to 1990. Even though f-gas emissions accounted for only 6.03% of overall GHG emissions, they showed a 35.09% increase compared to the year 1995 [6].

It is also interesting to observe that of all emissions, carbon emissions make the greatest contribution (by 69.17%) to greenhouse gas emissions, even though they have been decreased by about 33.02% compared to 1990 levels [6]. This stark decrease can be ascribed to various factors, with the most prominent factors being the improved living standards, the introduction and use of natural gas and renewables in the national energy system and residential heating, and the decommissioning of lignite-fired plants. In relation to the latter, Greece is implementing an extensive lignite phase-out plan in line with the Integrated Emission Directive 2010/75/EU, and, in the meantime, natural gas is being used as a transitional fuel. Initially, the year 2025 was established as the deadline for the complete decommissioning of lignite units, but in the meantime, natural gas imports have increased sharply, raising severe concerns. In order to restrain the galloping reliance on natural gas imports, an extension for lignite phase-out has been announced. The new timeframe for lignite decommissioning was recently extended to 2028, even though in January 2022, the percentage of lignite-fired electricity fell to the historical level of 12% [7].

Turning back to the contribution of sectors to GHG emissions in Greece in 2021, transportation, manufacturing industries and construction (and all of the remaining sectors) accounted for 31.42%, 9.11%, and 10.93%, respectively. As in other EU countries, in Greece, the decarbonization of transport remains a challenge, and it is the only sector among other combustion sectors that showed an increasing rate (13.49%) compared to 1990 levels. Of the other sectors, it is worthwhile to note that the contribution of the agricultural sector to GHG emissions showed a decrease of around 22.60%, which is mainly ascribed to the limited use of synthetic nitrogen fertilizers, the rise of organic farming, increases in fertilizer prices, the promotion of good farming practices, and the reduction of livestock production [6].

According to the report of the Greek Ministry of Environment and Energy [6], the reduction in GHG, which is partially driven by RES deployment, will continue over the next few years. The same conclusion was drawn by Chatzizacharia et al. [8], who employed the method of mixed autoregressive integrated moving average (ARIMA) to examine the causality and stationarity of climate indicators in order to forecast energy needs in the next few years in Greece. This research team observed that the gradual increased use of renewables will contribute to the reduction of greenhouse gas emissions. Although the energy needs of the country will increase in the years to come, they can be met sustainably if equal shares of renewables are deployed all over the country.

Despite supporting policies, the deployment of renewables is often inhibited by various factors. Most importantly, renewable types and, in particular, wind and solar resources cannot be available and predictable on a constant basis and are referred to as intermittent energy resources. Intermittency is a significant inhibiting factor in the adoption of renewable energy [9]. Other barriers involve the deficiencies in the legal framework for the specifications of renewable facilities, the high level of transmission losses, and the limited representation of local actors in spatial planning processes. The social acceptance of renewables has emerged as a critical factor over the last few decades [10]. In particular, there have been many cases where the implementation of renewable projects can be delayed or even blocked when the public responds negatively to proposed renewable facilities [11]. Public responses are often driven by concerns, which typically revolve around four areas. A prominent concern has to do with the visual impact of the renewable facility on the natural landscape, particularly in the case of large-scale wind and solar installations [12]. In relation to wind energy, the local public often complains about the noise produced during construction works, as well as the operation of the wind facility. Another important concern that can induce opposition focuses on the impact of renewable energy projects on the sustainability of the local natural environment and animal species [13]. Apart from these concerns, conflicts between the host communities and energy developers and the authorities occur in cases in which the planning and decision-making processes are regarded as unjust or when local representatives are not included in the processes [14]. Public concerns thus focus on various areas but, most importantly, indicate the strong effect of public acceptance on the implementation of renewable projects.

Even though many concerns about renewable energy surround the impacts of wind energy, the development of solar energy in rural settings has become a matter of criticism due to the threat it poses for agricultural land use [15,16]. A great deal of criticism focuses on the risks posed by the decrease in crop yields due to the installation of solar parks on productive farmland. There is already research showing that such installations reduce crop yields, and, as an example, a study in Germany indicated that this decrease can even reach 40% [17]. At the same time, unlike wind turbines, which can easily be combined with crop cultivation, it is not easy to co-locate agricultural production with solar panels [18], and, therefore, unless measures are taken, rural solar energy may further displace farmland and perhaps compromise food security [19].

Another prominent barrier to RES deployment concerns the cost of renewable energy production, which, particularly during the first years of RES deployment, was higher compared to the cost of fossil fuels [20]. The public sector is unable to cover the required capital for energy transition, meaning that private financing is necessary for greater renewable energy production [21,22]. Citizen investments in renewable energy can indeed provide significant capital [23], and an example of the positive contribution of private capital to national energy would be Germany, where citizens have supported the energy transition by participating in various feed-in-tariff schemes and own almost half of RES production [24]. Seen from this perspective, citizens are able to play a critical part in the deployment of renewables, and citizen investments could act as a lever for renewable energy deployment [23,24]. One could argue that households make a very minimal contribution, as most households install small-scale solar energy systems on roofs, which entail quite low installed capacities. However, this kind of installation leverage space cannot be used for other purposes, and, therefore, other useful spaces such as farmland can remain unaffected [25,26].

The potential benefits of citizen investments point to the need to pay more attention to the decision-making of citizens and understand the factors that affect it. Although literature on the subject is still somewhat limited, the existing research works have pointed to the influence of certain factors. In particular, the decision-making of citizens seems to be affected by both financial and non-financial factors. In terms of the latter, pro-environmental attitudes seem to render citizens more willing to make investments in renewables. According to Gamel et al. [27], environmental attitudes can drive investments even if there are disadvantages such as lower return on investments or delayed payments. The earlier study by Faiers and Neame [28] showed that adopters of solar photovoltaics were driven predominantly by the pro-environmental aspect of the systems and their inclination to lead a more sustainable life. Moreover, awareness and concerns about environmental issues and climate change have been indicated as strong drivers of renewable system adoption [29,30]. However, other studies have shown that positive environmental attitudes are not always translated into investments in renewables, because financial aspects of the investment can sometimes override the environmental ones [31,32].

Among the financial aspects, return on investment has a strong influence on investment decisions, with investors preferring high returns and being mindful about the certainty of returns [21,33]. Incentives can also be highly effective in prompting individuals to proceed to RES investments [32]. There are, however, factors that inhibit investments, with the most important being the cost of investment [32], the risk of the investment, and the lack of savings or financial means [33,34]. In relation to the latter, the possession of additional income can motivate individuals to invest because the investment is regarded as a way to decrease future energy expenses [28] or become independent from electricity suppliers [29]. Finally, citizens’ socio-demographic profile seems to be affecting investment decisions, with the most influential socio-demographic variables being age, gender, educational background, annual income, employment, and family status and house ownership. In terms of age, older age is discouraging for RES investments, as older people are significantly less willing to invest [27,35,36,37]. One explanation could be that older people are deterred by the long payback periods and avoid any changes in their habits that could be caused by adopting a renewable system [36]. That being said, other studies have found a positive correlation between older age and the adoption of renewable technologies [28,30,38]. Findings on the effect of income on RES investments, however, have been consistent in indicating that high income renders individuals more likely to invest in renewables [28,30,37]. Other influential demographic variables involve education level [30,32,37], gender [21], place of residence [30,39], and residence ownership [30,38,39].

It can be seen that there is a broad array of factors, both financial and non-financial, that influence citizens’ decision to invest in renewable energy. However, the factors that affect RES investments constantly change, and it is necessary to conduct research regularly so that policymakers have the necessary knowledge at their disposal to improve existing policies and to design new ones. Moreover, most of the existing studies were conducted in northern countries of the European Union, and there is a paucity of relevant studies in southern EU countries, even though they have great renewable potential. As an example, Greece has impressive renewable potential and has the resources to become an RES-blessed, energy-rich country [40].

In view of the potential contribution of citizen RES investment to the reduction of GHG emissions, this study examines the effect of factors whose influence has been established but also tests factors that have not yet been examined in the somewhat limited willingness-to-invest literature. In particular, our analysis adds to the existing knowledge by examining certain understudied sociodemographic and attitudinal variables. Another contribution to the extant literature is that the study examines in great detail the role of information on willingness to invest, and it also examines the effect of citizens’ satisfaction with the media’s information on RES investments.

The approach of logistic regression was employed because it enables us to calculate the probability of investing in RES based on a particular dataset of independent variables, including financial, sociodemographic and attitudinal variables. Insights from this study may enable a better understanding of citizen investors’ decision-making and may also reveal guided efforts to improve the existing investment environment both in the study area and elsewhere.

2. Materials and Methods

2.1. Study Area and Research Instrument

The findings reported in this paper consist part of a broader study that was conducted from May 2020 to May 2021. Its focus was on citizens’ attitudes towards renewable energy investments. The study area was Greece, as it represents a unique case study, mainly for two reasons. First, it is located in the Mediterranean, which is expected to experience intense impacts from climatic change. Its eastern basin is especially vulnerable to climate change, and it seems that this area is warming at a quicker pace in comparison to the global average increase in temperature, with climate models predicting an increase of 3.5–4.0 °C, as well as a 30% rainfall decrease [41]. Second, Greece has excellent conditions for developing an energy system that will rely on renewable energy. The country’s topography and climatic conditions are optimal for renewable energy production. In terms of wind energy, the potential is massive, especially in the islands of the Aegean Sea, where the average wind speed corresponds to about 62–88 km/h. The solar potential is equally promising, as the country’s average solar radiation per year is around 1570 kWh/m². In many regions, annual sunshine hours exceed 2700 h, or 7.5 h a day, whereas annual sunshine in Southern Aegean is about 3100 h, or 8.5 h a day [42,43].

A structured questionnaire consisting of 26 closed-ended questions was constructed explicitly for the purposes of this research. In order to determine the content of the questionnaire, the relevant literature was taken into account. In particular, the focus was on research papers delving into investors’ and potential investors’ profiles, investment preferences, and perceived challenges and problems in implementing renewable energy investments (such as the research works of Faiers and Neame [28], Aguilar and Cai [21], Willis et al. [35], and Masini and Menichetti [44]). In order to enable respondents to express their views with greater precision, most questionnaire items employed five-point Likert scales [45].

To verify that the questionnaire was able to provide precise and coherent results, a pilot study had to be conducted on a limited scale before the performance of the actual study [45]. In addition, a pilot study, which must have the same characteristics and follow the same steps as the actual study, helps researchers discover the strong and weak points of a questionnaire. In other words, the research instrument is put to the test in order to make improvements where necessary. In our questionnaire, a few phrases in the items were reworded, as they were not understood by all pilot study participants. In addition, the response scale for one item had to change to facilitate responses and the order of three items had to be re-arranged to ensure the coherence of the questionnaire and to avoid confusion among respondents.

In compliance with Law 4521/2018 and, in particular, with the provisions set forth in Article 23, the research had to obtain a permit from the Research Ethics Committee of Democritus University of Thrace. Hence, a request for approval containing the research design and research instruments was submitted to the Committee, which decided unanimously in Decision No. 3/09-12-2019 to approve the performance of this research.

2.2. Sampling Process

The studied population comprised all Greek citizens, and the study area was the whole country. In order to produce a representative sample, simple random sampling was used in recruiting respondents [46]. In this way, the findings from this study can be generalized with confidence to the entire population. Another advantage of this sampling method is that it does not require detailed knowledge about the population under study.

Even though the method of simple random sampling with no replacement was employed, the finite population correction may be disregarded because the sample size (n) is small compared to the population size (N) [47].

$$n=\frac{t^2\cdot \overline{p}\cdot \left(1-\overline{p}\right)}{e^2}=\frac{1.96^2\cdot 0.50\cdot \left(1-0.50\right)}{0.025^2}\cong 1536$$

In the above formula, t represents the value of the Student’s t-distribution for a probability of (1 − α) = 95% with n − 1 degrees of freedom. The latter does not appear in the formula, as it refers to the number of independent values that a statistical analysis can estimate, i.e., the number of values that are free to vary as parameters are estimated. Because the size of the performed pre-sampling is quite high (greater than 50), the value of t is obtained from probability tables for normal distribution for the pursued probability. Practically, for a probability of 95% the value is 1.96 [45,47]. In addition, p stands for the estimation of proportion and e represents the greatest acceptable difference that occurs between the unknown population mean and the sample mean. Therefore, it may be accepted that e corresponds to 0.025, which is 2.5%.

In order to estimate the sample size, it was necessary to carry out pre-sampling with a sample of 50 respondents. For every variable, therefore, the actual analogy of the population was estimated. The use of questionnaires in research should not be limited to the estimation of only one variable but rather of more variables. For this reason, it was necessary to estimate the sample size for each variable. “Gender” was the variable that yielded the highest sample size. If the estimated sample sizes are close, do not differ significantly, and fall within the economic ability of the research, then the highest sample size is chosen. By doing so, the variable exhibiting the highest variability and the remaining variables are estimated with a high level of precision [45].

The most significant step in sampling is to establish the sampling frame. Perhaps one of the most proposed approaches to recruiting a simple random sample is to utilize tables of random numbers or to use computer-produced inventories of random numbers [48]. In large-scale studies, a large, voluminous list of all citizens in every prefecture and in alphabetic order must be created, and, at the same time, each citizen must be given a unique code number. However, such a process would be too time-consuming, expensive, and difficult. Even if this process was followed, there would be no guarantee that the citizen in question would be alive or would be found at the location registered on the list [47].

In order to establish the sampling frame for this study, the results of the 2011 national census were used. According to the process that was followed, each citizen was treated as a unique case and had a number that corresponded to the region, prefecture, municipality, and local community in which they resided. Of course, the name of the citizen was kept unknown, as it was not necessary to know it unless the specific number was chosen. The subjects to be recruited were enrolled on lists. That being said, it was expected that, due to the numbers of deaths and births and internal or external population migration, the real number of the population of municipalities would be different than the one recorded on the census [49]. To ensure that all respondents understood the questions and to avoid any incomplete responses, all questionnaires were completed through personal interviews, and, in total, 1536 citizens participated in the study.

2.3. Data Processing

The collected data were entered into Microsoft Excel and analyzed with the Statistical Package for Social Sciences (SPSS, version 22). First, descriptive statistics were run on all variables in order to form an overall picture of the data. Besides descriptive statistics, the Chi-square test was applied to determine the dependence of the outcome variables (i.e., willingness to invest in renewables) on the sociodemographic variable of “income” at a significance of p < 0.05 [45].

Then, in order to achieve the aim of the study, which was to detect the factors that predict the willingness of citizens to invest in renewable energy, logistic regression was conducted. Logistic regression was considered appropriate because it can analyze a dataset where one or more independent variables define a specific outcome [50]. In simpler terms, logistic regression can be applied to estimate the probability of an event; in our case, it can predict the probability of investing in renewable energy. The model derived from logistic regression can be applied widely due to its algorithmic efficacy and capability of addressing complicated nonlinear problems. This is achieved by adding a suitable linking function to the usual linear regression model [50]. Moreover, logistic regression calculates parameters using the likelihood ratio. By doing so, the responsive variable acts as a likelihood function that predicts the category of a given observation [51].

There are various methods for choosing independent variables. In this study, the method of stepwise forward selection was applied. The method starts only with the constant (unless the researcher does not prefer to include the constant in the analysis) [50] and, in each step, the method introduces a variable with the lowest level of statistical significance for the respective Rao’s statistic under the condition that it does not exceed a certain limit (for instance, 0.005). The introduced variables are checked in terms of whether they meet an exclusion criterion. If the Wald statistic is used as the criterion, then the Wald statistics of all introduced variables are monitored and variable(s) with the highest level of statistical significance are eliminated, provided that it does not exceed a limit value (such as 0.001). If there are no variables that meet the exclusion criterion, the procedure continues with the introduction of new variables. If, however, a variable has been chosen to be removed and the model results in a previous one, the selection of variables stops; otherwise, the model is estimated without this variable and the other variables are checked for removal. This process continues until no further variables can be removed from the model and the re-evaluation of variable introduction starts again [50]. This method led to a model that consisted of nine independent variables, which provided a suitable interpretation of results.

Multicollinearity is a problem that emerges often in regression analyses, and it emerges if two or more predictor variables are highly correlated to each other [52]. If they are highly correlated, they are not able to provide unique or independent information in the regression model. Multicollinearity between variables decreases the reliability of the model [53]. In order to ensure that the independent variables could be used in the analysis, the variance inflation factor (VIF) was performed. This test is a common indicator to detect multicollinearity and to examine the correlation and strength of correlation between the predictor variables in a regression model. The results of the analysis showed that there was no multicollinearity problem. Every independent variable in the model had a VIF of < 5 and a tolerance of >0.1. Hence, every variable was independent and could be used in a predictive analysis (the results of the VIF are presented in

Table A1. Collinearity statistics.

Variables	Tolerance	VIF
Occu	0.963	1.038
Q3	0.914	1.094
Q12_SAT	0.917	1.091
Info_1	0.929	1.077
Info_4	0.943	1.060
EconReason_1	0.785	1.274
EconReason_2	0.866	1.155
EnvReason_2	0.765	1.308
SocReason_2	0.753	1.328

in the Appendix A).

Finally, for each independent variable, our analysis reported the values of the beta coefficients, standard error, degrees of freedom, p-value, and odds ratios. In order to calculate the odds ratios, the expected values of the beta coefficients (Exp(B)) were estimated for every variable.

2.4. Measures

To perform logistic regression, the dependent variable was “citizens’ willingness to invest in renewable energy” (V_RES). The independent variables involved both categorical and continuous variables. The categorical variables used were occupation (Occu), agreement with the installation of solar or wind parks to a location from which the installation would be noticeable at respondents’ residences (Q3), and satisfaction with the media in terms of the information they provide on investments in renewable energy (Q12_SAT).

In addition to these, the continuous variables “Info_1”, “Info_4”, “EconReason_1”, “Econ Reason 2”, “EnvReason_2”, and “SocReason_2” were used as independent variables. These were extracted from factor analysis performed on the following multivariates: “information sources”, “economic reasons for investing in renewable energy sources”, “environmental reasons for investing in RES”, and “social reasons for investing in RES”. In relation to the first three multivariates, the questionnaire involved multivariate questions that required respondents to mark on a five-point Likert scale (ranging from “Strongly disagree” to “Strongly agree”) the degree to which they agree with various economic, environmental, and social reasons for investing in renewables. Regarding “information sources”, respondents rated the frequency (five-point scale ranging from “Seldom” to “Never”) with which they use various information sources to obtain information about environmental and energy topics.

Regarding the multivariate “economic reasons for investing in RES”, before the application of factor analysis, certain tests were conducted in order to ensure that the data were suitable for factor analysis. In particular, the Cronbach’s alpha value (0.883), Keiser–Meyer–Olkin index (0.888), and Bartlett’s test of sphericity (Chi-square = 5932.216) confirmed the suitability of the data. The performance of factor analysis extracted two factors (referred to as “EconReason_1” and “EconReason_2”). Factor EconReason_1 was termed “subsidies, low investment taxation, and improved income and electricity cost” since it included variables related to the following economic benefits of the investment: “income increase through selling produced energy”, “high subsidies granted for RES investments”, “reduction in electricity costs”, “low taxation on RES investments”, and “acquisition of stable income”. The second factor, EconReason_2, was termed “optimal investment opportunity and protection from oil price fluctuations” because it involves variables related to aspects that make RES investment optimal. In particular, it included the following variables: “relatively low investment cost compared to other investment types (e.g., asset purchase)”, “short depreciation time”, “capital investment”, and “protection from oil price fluctuations due to geopolitical crises”.

The multivariate “environmental reasons for investing in RES” yielded two factors. The Cronbach’s alpha value (0.877), Keiser–Meyer–Olkin index (0.678), and Bartlett’s test of sphericity (Chi-square = 5005.828, with df = 6 and p = 0.000) confirmed that the data were suitable for factor analysis. The first factor included the variables “flora protection” and “fauna protection” and thus was named “flora and fauna protection”, whereas the second factor involved the variables “contribution to the reduction of air pollution” and “contribution to the mitigation of the depletion of natural resources” and thus was named “mitigation of air pollution and natural resources depletion”.

Regarding the multivariate “social reasons for investing in RES”, factor analysis resulted in two factors. The Cronbach’s alpha value (0.802), Keiser–Meyer–Olkin index (0.712), and Bartlett’s test of sphericity (Chi-square = 3283.840, with df = 10 and p = 0.000) confirmed that the data of Q9 were suitable for factor analysis. The variables “increasing respect from friends and acquaintances” and “social prestige through entrepreneurial activity” fell under the first factor, which was named “boosting social profile”. The second factor included the variables “desire to adopt pro-environmental behavior”, “desire to set a good example for my family” and “desire to set a good example for society”. Based on the content of its constituent variables, the second factor was termed “pro-environmental behavior and setting an example”.

The multivariate “information sources” yielded four factors. Before the application of factor analysis, the eligibility of the data was confirmed through the Cronbach’s alpha value (0.844), Keiser–Meyer–Olkin index (0.825), and Bartlett’s test of sphericity (Chi-square = 6193.234, with df = 55 and p = 0.000). The variables “websites of official organizations”, “news websites”, and “general websites” fell under the first factor, which was labeled “Internet-based sources” (Info_1). The second factor involved the variables “education”, “universities—research institutes”, and “family and friends” and was labeled “education and close environment” (Info_2). The third factor included the variables “banks”, “company leaflets”, and “environmental organizations” and was labeled “private stakeholders” (Info_3). Finally, the fourth factor involved the variables “television—radio” and “newspapers and magazines” and was labeled “mass media” (Info_4). A list with the independent variables included in the logistic regression model is provided in

Table A2. Independent variables included in the logistic regression model.

Model Variables	Description
Occu	Respondents’ occupations
Q3	Respondents’ level of agreement with the installation of solar or wind parks near their residence
Q12_SAT	Respondents’ level of satisfaction with the media in terms of the information they provide on investments in renewable energy
Info_1	Internet-based sources
Info_4	Mass media
EconReason_1	Subsidies, low investment taxation, and improved income and electricity costs
EconReason_2	Optimal investment opportunity and protection from oil price fluctuations
EnvReason_2	Mitigation of air pollution and natural resource depletion
SocReason_2	Pro-environmental behavior and setting an example

in the Appendix A.

3. Results

3.1. Respondents’ Sociodemographic Characteristics

The sociodemographic variables collected in this study were gender, age, occupation, educational background, marital status, place of residence, and annual income. Women (51.6%) slightly outnumbered their male counterparts, and, with regard to age, a significant share (27.9%) was aged 41 to 50 years old, whereas somewhat lower shares were aged 31 to 40 years (22.1%), 18 to 30 years (21.9%), and 51 to 60 years (18.3%). In terms of occupation, the percentages of employees in the private and public sectors (21.2% and 19.9%, respectively) were greater in comparison to other occupation categories. More specifically, appreciable percentages of respondents were pensioners (16.8%), unemployed (13.2%), and freelancers (12%). However, only as few as 7.6% of the respondents were involved in crop and livestock farming, which is compatible with official data showing the abandonment of agricultural activities over the last few years.

In terms of education level, university graduates and upper secondary school graduates comprised 22.3% and 20.8% of the sample, respectively. Regarding respondents’ marital status, slightly more than half of the respondents reported being married (51%), and most of these respondents reported having two children (28.3%). With regard to the respondents’ place of residence, most respondents were urban dwellers (64.1%) and, in terms of annual income, 28.5% reported earning an annual income of EUR 10,001–20,000 and 20.1% reported earning EUR 5001–10,000. It should, however, be noted that a considerable percentage of respondents (28.8%) did not report their annual income. A detailed table with respondents’ sociodemographic characteristics can be found in a previous publication [54].

3.2. Citizens’ General Views about Renewable Energy Sources

The questionnaire started with a section containing introductory questions about renewable energy sources, which served to introduce respondents to the topic of the survey and collect information about their attitudes towards renewables. One of these questions examined respondents’ attitudes towards the installation of solar or wind parks in a location from which the installation would be noticeable from their residence. It was indicated that respondents’ opinions seemed to be somewhat divided. In particular, a significant proportion of respondents neither agreed nor disagreed (30.7%), whereas the percentage of those who disagreed was substantial (28%).

Citizens’ satisfaction with the media in terms of the information they provide on investments in renewable energy was also examined (represented as “Q12_SAT” in the model). Even though all variables received low ratings, the lowest satisfaction was observed for information that the media provide about changes in the institutional framework (65%), followed by information on the institutional framework of renewable energy (tax system, national legislation, and integration of European Directives) (64.9%) and loan opportunities and investment conditions (63.1%). Slightly higher satisfaction was observed for media information about the economic, environmental, and social advantages of renewable energy (13.8%) (

Table 1. Percentage units related to respondents’ satisfaction level with media information on renewable energy investments.

	Very Dissatisfied	Dissatisfied	Moderate Satisfaction	Satisfied	Very Satisfied
Institutional framework of renewable energy	30.9	34.0	27.7	5.9	1.4
Changes that occur in the institutional framework	29.2	35.8	27.9	6.3	0.8
Available subsidies for investments in renewable energy	28.6	32.0	29.0	9.1	1.3
Loan opportunities and investment conditions	30.3	32.8	26.6	8.1	2.1
Economic, environmental, and social advantages of renewable energy	25.1	29.4	31.7	12.0	1.8

).

3.3. Respondents’ Willingness to Invest in RES

Respondents’ willingness to invest in RES was examined next, and it was indicated that the strong majority of respondents would make an investment in renewable energy (78.7%), whereas significantly fewer respondents (21.4%) were not willing to make any investment (Figure 1).

Of those willing to invest, most would mostly invest low sums of money. That is, 17.9% would invest between EUR 500 and 1000, 17.2% would invest between EUR 1000 and 2000, and 15.6% would invest less than EUR 500. Only 4.6% would make an investment of more than EUR 20,000 (

Table 2. Respondents’ willingness to invest various sums of money.

Unwilling to Invest	<EUR 500	EUR 500–1000	EUR 1000–2000	EUR 2000–5000	EUR 5000–10,000	EUR 10,000–20,000	>EUR 20,000
21.4	15.6	17.9	17.2	11.1	8.3	4.0	4.6

).

Then, in order to examine the relationship of respondents’ income with their willingness to invest, a Chi-square test was carried out. The analysis showed a significant relationship between respondents’ willingness to invest and their income. In particular, citizens who were unwilling to invest dominated low-income categories, whereas high-income categories were dominated by citizens who were willing to invest (Chi-square = 14.473, df: 5, p < 0.05) (

Table 3. Relationship between willingness to invest and respondents’ income.

		Willingness		Total
		Unwilling	Willing
Income	<EUR 5000	46	123	169
		14.0%	10.2%	11.0%
	EUR 5001–10,000	64	244	308
		19.5%	20.2%	20.1%
	EUR 10,001–20,000	94	343	437
		28.7%	28.4%	28.5%
	EUR 20,001–30,000	15	105	120
		4.6%	8.7%	7.8%
	>EUR 30,000	6	53	59
		1.8%	4.4%	3.8%
	Prefer not to disclose income	103	340	443
		31.4%	28.1%	28.8%
Total		328	1208	1536
		100.0%	100.0%	100.0%

).

3.4. Predicting Investments in RES (V_RES)

For predicting citizens’ willingness or unwillingness to invest in renewables, the dependent variable was “citizens’ willingness or unwillingness to invest in RE” (V_RES).

Table 4. Logistic regression model output.

Omnibus Tests of Model Coefficients
		Chi-Square	Degrees of Freedom	Significance
Step 9	Step	4.586	1	0.032
	Block	754.406	9	0.000
	Model	754.406	9	0.000
Model Summary
		−2 Log likelihood	Cox and Snell R Square	Nagelkerke R Square
	Step 9	1374.942	0.388	0.517
Hosmer and Lemeshow Test
		Chi-square	Degrees of Freedom	Significance
		7.090	8	0.527

illustrates the output of the analysis and, more specifically, the model fit tests and statistics. It can be seen that the Omnibus test of the model coefficients yielded a Chi-square of 754.406 with nine degrees of freedom at a significance level of 0.000. According to the null hypothesis, coefficient β1 is zero; that is, there is no statistically important association between the predictor variable, x, and the response variable, y. In this analysis, the null hypothesis is rejected because the addition of variables to the model did not markedly affect the ability to predict citizens’ willingness or unwillingness to make investments in renewable energy.

The model summary in Table 4 shows the model’s goodness of fit. The goodness of fit was assessed using the Hosmer–Lemeshow statistic test, the test of change in −2 log likelihood (−2-LL), Cox and Snell R square, and Nagelkerke R square. The −2 log likelihood statistic was 1374.942. The model had significant predictability when this value was compared to the −2 log likelihood for the null model in the Omnibus test of the model coefficients. The R² values (Cox and Snell, and Nagelkerke) can approximate the variation for which the model can account. The value of the Nagelkerke R² showed that the model accounted for 51.7% of variance. The Hosmer and Lemeshow test indicated the model’s goodness of fit. Given that the value of Chi-square = 7.090, it denotes a statistical significance higher than 0.05.

The classification table (

Table 5. Classification table.

Observed			Predicted
			Invest in RES		Percentage Correct
			Unwilling	Willing
	Invest in RES	Unwilling	57	271	17.4
Step 9		Willing	42	1166	96.5
Overall percentage 79.6

) presents the observed and predicted willingness of citizens to make investments in renewables. Model performance was also assessed with the percentage of correct prediction. The overall percentage of correct prediction was estimated as 79.6% (observations correctly classified).

Table 6. Results of logistic regression analysis.

Model Variables	B	S.E.	df	p-Value	Odds Ratio
Occu	−0.085	0.021	1	0.000	0.521
Q3	0.248	0.049	1	0.000	0.438
Q12_SAT	0.517	0.070	1	0.000	0.374
Info_1	0.169	0.069	1	0.015	0.458
Info_4	−0.145	0.068	1	0.033	0.536
EconReason_1	0.181	0.070	1	0.010	0.455
EconReason_2	0.487	0.071	1	0.000	0.381
EnvReason_2	−0.217	0.079	1	0.006	0.554
SocReason_2	0.288	0.074	1	0.000	0.428

shows the final model, and it can be seen that nine independent variables were significant: Occu, Q3, Q12_SAT, Info_1, Info_4, EconReason_1, EconReason_2, EnvReason_2, and SocReason_2. Hence, the equation of logistic regression takes the following form:

V_RES = −0.085 Occu + 0.248 Q3 + 0.517 Q12_SAT 0.169 Info_1 −0.145 Info_4 + 0.181 EconReason_1 + 0.487 EconReason_2 −0.217 EnvReason_2 + 0.288 SocReason_2

It should be specified that the continuous output of the logistic model was mapped to the two discrete classes of willing and unwilling to invest. The p-value of the independent variables included in the model indicates that all independent variables were statistically significant since their p-values were less than 0.05. Moreover, the beta coefficients refer to the logistic transformation of the independent variables. In particular, Occu (β = −0.085), Info_4 (β = −0.145), and EnvReason_2 (β = −0.217) had a negative effect on the logistic transformation and generally on the dependent variable. However, the remaining variables presented a positive effect. Regarding Q3, the analysis indicated that citizens are more likely to invest when they agree with the construction of wind or solar parks in locations that would be visible from their residence (β = 0.248). In addition, the probability of investing in renewables increases when citizens obtain information about environmental and energy topics from Internet-based sources (β = 0.169) but decreases when this information is obtained from mass media (β = −0.145). Another influential factor concerns the satisfaction of citizens with the media in terms of the information it provides on renewable investments. In particular, as satisfaction with media’s information about renewable investments increases, the probability of proceeding to investments in renewable energy also increases (β = 0.517). Factors concerning economic reasons for investing were also found to predict investments. That is, subsidies, low investment taxation, and improved income and electricity cost (β = 0.181), along with optimal investment opportunity and protection from oil price fluctuations (β = 0.487), were positively related to investments. The higher the importance that citizens attach to these factors, the more likely they are to proceed to investments.

The odds ratios of the independent variables show the likelihood that they were associated with willingness to invest. In particular, Q3 (agreement with the installation of wind/solar parks near respondents’ residences) had an odds ratio of 0.438. This indicates that an increase of one unit on the five-point response scale would increase the likelihood of investing in renewables; that is, the likelihood of investing would be 0.438 times higher. For respondents’ occupation, the odds ratio, which was 0.521, suggests that for each occupation category, the odds of investing in renewable energy decrease by 0.521 times. In the questionnaire, the order of occupations was “public servant”, “private employee”, “freelancer”, “entrepreneur”, “homemaker”, “crop farmer”, “livestock farmer”, “pensioner”, and “unemployed”. Moving from one occupation category to the other, the probability of investing is reduced by 0.521. It thus seems that unemployed citizens, pensioners, and farmers are notably less likely to invest compared to citizens employed in the public and private sectors.

In addition, respondents’ satisfaction with the media’s information on RES investments (represented as “Q12_SAT” in the model) had an odds ratio of 0.374, showing that satisfaction with the media’s information increases the likelihood of investing. More specifically, every increase of one unit on the five-point satisfaction scale increased the likelihood of investing by 0.385 times. In relation to information, two factors emerged as statistically important: Info_1, which expresses information from Internet-based sources (such as websites, social media, and so on), exerted a positive effect on the dependent variable and had an odds ratio 0.458, meaning that the more frequently citizens use the Internet for their information, the more likely they are to invest. Conversely, information from the mass media had a negative effect on the dependent variable, suggesting that when citizens use mass media frequently the likelihood of investing decreases by 0.536 times.

Moreover, the independent variable EconReason_1 had an odds ratio of 0.455, meaning that citizens are 0.455 times more likely to invest when they agree with the reasons included in the factor EconReason_1. That is, the more citizens agree that their investment decision is affected by “subsidies, low investment taxation, and improved income and electricity cost”, the more likely they are to proceed to investments. For each increase on the five-point agreement scale, the likelihood of investing in renewables increased by 0.455 times.

The same applies to EconReason_2, which had an odds ratio of 0.381, suggesting that the likelihood of investing for each unit increases by 0.381 times. More analytically, the higher the agreement of citizens with the ability of investments to provide an optimal investment opportunity and to protect them from oil price fluctuations, the higher the likelihood of proceeding to investments.

The independent variable EnvReason_2 had an odds ratio of 0.554. Given that this variable had a negative beta coefficient, it indicates that citizens are 0.554 times less likely to invest when they agree that their decision is affected by RES investments’ contribution to air pollution mitigation and natural resource depletion. Finally, SocReason_2 had an odds ratio of 0.428, showing that citizens are 0.428 more likely to invest when they perceive that RES investments are a way to adopt pro-environmental behavior and set a good example for other people.

4. Discussion

Investments in renewable energy have the ability to contribute to the reduction of greenhouse gases and thus help with the difficult process of decarbonizing the energy system. In this respect, citizens that invest in renewables can facilitate the deployment of renewables by becoming providers of a major portion of the required capital [24]. From the perspective of policy, research on what affects citizens’ investment decisions assumes considerable importance, as it reveals what motivates or prevents citizens from investing. To put this simply, research of this kind can show policymakers where to direct policy efforts in order to correspond to citizens’ expectations and, in this way, transform willingness to invest into real investments while addressing perceived barriers. Seen from the perspective of social research, investments in renewable energy are an exceptional case because they are the only type of investment in the disposal of citizens that combines financial and environmental aspects. In other words, it is highly interesting to measure attitudes towards a type of investment that is not solely driven by financial but also by environmental motives [27,28,29,30].

In relation to financial motives, this study is consistent with previous studies showing that the economic profitability of RES investments is a major driver of citizen investment in renewables [31,32]. Our study offers a more precise picture of the economic factors that motivate citizens to invest, as it indicates that citizens are more likely to invest when offered high subsidies and when they regard the investment as a means to acquire a source of steady income (by selling the electricity they produce to the grid), as well as to reduce their households’ electricity costs. Not surprisingly, low taxation on the investment would also render citizens more likely to invest. One could thus argue that, to make RES investments attractive, policymakers need to ensure that investments are favorable in terms of these economic aspects. Our findings, however, show that citizens are not affected only by the usual economic factors like subsidies and income but also by investment-focused factors such as depreciation time, investment cost, and capital investment. It is thus possible that citizens take into account equally economic- and investment-focused factors, which of course makes the task of creating an attractive investment environment even more challenging.

The willingness-to-invest literature has established the effect of both financial and non-financial factors on individuals’ willingness to invest in renewable energy with non-financial factors involving an impressively wide array of sociodemographic and attitudinal variables [27,28,35,36,37]. Although the effect of information about RES investments had not been looked into, our analysis identified the relevant role of the information sources that citizens use to learn about energy and environmental topics. In particular, obtaining information about such topics from Internet-based sources exerted a positive effect on the investment probability, highlighting the positive role of these sources on investments. This is a particularly interesting finding because it brings forward the significant role of Internet sources in informing potential investors about the existing investment environment. However, Internet information is not always regulated for quality or accuracy, and, therefore, it is very important that an official body evaluate the resource or information and ensure that the provided information is accurate. In contrast to the positive effect of Internet-based sources, the use of mass media was found to affect investment decisions negatively. One explanation may be that mass media’s coverage of RES investments may be insufficient or that the information does not communicate the benefits of the available investment options. In this regard, citizens interested in investing may not feel that they have enough knowledge to proceed to investments or may be discouraged. In addition, willingness to invest was positively affected by the overall satisfaction of citizens with mass media’s coverage of RES investments. Hence, a new message from this study is that certain information sources may be affecting investment decisions more than was initially thought, and the media’s efficiency in covering RES investments is relevant and should be addressed if any progress towards citizen investment is to be achieved.

In relation to sociodemographic characteristics that affect investment decisions, our analysis adds to the existing literature by indicating the influence of citizens’ occupations. As the value of this variable increased, the likelihood of investments in renewables decreased and, for each occupation category, the odds of investing in renewable energy decreased, meaning that pensioners and unemployed individuals are the least likely to invest. For unemployed citizens, the low likelihood of investing is of course reasonable and expected, but for pensioners some explanations are required. A possible explanation would be that pensions in Greece have been curtailed significantly during the decade-long economic crisis and thus, pensioners may lack the financial means to proceed to an investment in renewable energy. Regardless of the validity of this explanation, pensioners’ older age in relation to other categories may be rendering them unwilling to invest. This explanation corroborates studies showing that older people are significantly less willing to invest [27,35,36,37], possibly because they are discouraged by long payback periods or avoid new electricity production technologies that alter their usual habits [36].

Another finding that ought to be discussed concerns the effect of citizens’ agreement with the installation of renewable facilities near their residence. Interestingly, those who agree are more likely to invest in renewables compared to those who disagree with the placement of RES facilities near their residence. Our results suggest that the acceptance of renewable facilities near citizens’ residences is positively associated with willingness to invest. In this regard, it is possible that citizens are more likely to invest when they are not annoyed by the installation of such facilities in locations near their residence. One may assume that this is an indication of “not in my backyard” (NIMBY), which describes residents’ opposition to proposed facilities necessary for society’s welfare in their local area, which is often driven by selfish motives. However, such an assumption could not be based on the evidence from the present study because our analysis did not focus on the siting of renewable energy facilities but rather predominantly examined the siting of RES facilities as a factor affecting citizens’ willingness to invest in renewable energy.

Finally, certain study limitations and recommendations for future studies must be stated. One limitation is that this study examined citizens’ willingness to invest in renewables in general rather than specific renewable types. An examination of factors affecting investments in specific types, however, could guide policymakers to improve existing policies with higher precision. In addition, in relation to the cross-classification of the observed values with the predicted values for the dependent variable, it should be noted that the model performed very well in predicting those willing to invest, but it did not perform equally well for those unwilling to invest, as it identified only 17.4% of those unwilling to invest. Another limitation concerns the fact that the study was conducted a few months before the war in Ukraine, which resulted in major increases in energy prices and highlighted the problem of expensive fuel imports. Such price changes may have affected citizens’ willingness to invest and may have rendered citizens more positive to the idea of becoming independent of non-renewable energy sources and fuel imports. For this reason, it would be relevant to conduct a similar study again after the war is over when prices have returned to pre-war levels. Moreover, it would be worthwhile to compare investors’ preferences for various investment options, as this research focused explicitly on investments in renewable energy. That is, it would be interesting, from a policy perspective, to understand whether citizens prefer RES investments and how they rank them in relation to other investments such as assets and bonds. Such a comparison could reveal the competitiveness of renewable energy investments and lead to conclusions about the effectiveness of the applied strategies. Moreover, since most literature on the subject is based on quantitative research designs, possible nuances in potential investors’ decision-making may have been missed, but they may be captured through qualitative methods such as in-depth interviews. In addition, as our results indicated a positive effect of the acceptance of renewable facilities on citizens’ willingness to invest, it seems that it may be worthwhile to examine the social acceptance of renewable facilities and understand further its effect on the implementation of large-scale renewable energy projects in Greece. Given that the investment environment in Greece is constantly changing and adapting to the directives of the European Union, it is recommended to repeat this study after at least five years in order to examine whether and in what ways the factors affecting citizens’ willingness to invest have changed. Some factors that may be worthwhile to examine in the future involve changes in the institutional framework that are currently being promoted and new investments schemes, such as net-metering, which has recently become available for citizens.

5. Conclusions

The purpose of this study was to examine factors that affect the willingness of citizens to invest in renewable energy. Research on this subject can be considered necessary because RES investments can become an effective tool to enhance the progress towards the reduction in GHG emissions. In addition, the factors that affect RES investments constantly change, and it is thus necessary to conduct research regularly so that policymakers have the necessary knowledge in their disposal to improve existing policies and design new ones. The majority of Greek citizens generally feel positive towards investments, which is promising for citizens’ potential role in facilitating the transition towards a low-carbon energy system. Among the variables included in our logistic regression model, economic and investment factors appeared to have a significant impact on citizens’ willingness to invest in renewable energy. Hence, as potential investors, citizens take into account both economic- and investment-focused aspects, and if they are regarded as favorable, they are more likely to invest. Our analysis adds to the existing literature by capturing the significant impact of information on willingness to invest, which was, in particular, found to be affected by respondents’ choice of information sources as well as the media’s efficiency in covering RES investments. Since the results here stress the significance of information, policymakers interested in increasing citizen investments should ensure the provision of updated information about renewable energy investments. Internet-based sources exerted a positive effect on investment decisions, and this brings forward the potentially central role of the Internet in turning willingness to invest into actual investments. In addition, more research on the relationship between willingness to invest and the acceptance of renewables is required because this study indicated that citizens agreeing with the installation of renewable facilities near their residence were more willing to invest. In other words, the extent to which siting considerations affect investment decisions is worthwhile to examine in more detail in future research.